{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0f9e82d1-f3e8-4875-87aa-b1cfa7498c36",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "信计221======================刘显婷==================================224180117\n",
      "回归模型: s1 = 0.65 * j3 + 26.44\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('信计221======================刘显婷==================================224180117')\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import font_manager\n",
    "# 文件路径\n",
    "file_path = r\"D:/Users/19202/Desktop/highschool.txt\"\n",
    "# 使用numpy的loadtxt函数读取文本文件中的数据，跳过第一行,标题行\n",
    "data = np.loadtxt(file_path, skiprows=1)\n",
    "# 提取数据中的第一列作为初三成绩数据\n",
    "j3 = data[:, 0] \n",
    "# 提取数据中的第二列作为高一成绩数据\n",
    "s1 = data[:, 1] \n",
    "# 使用numpy的polyfit函数进行一次多项式拟合（也就是线性拟合），得到拟合直线的斜率a和截距b\n",
    "a, b = np.polyfit(j3, s1, 1)\n",
    "# 打印拟合得到的回归模型表达式，保留两位小数\n",
    "print(f\"回归模型: s1 = {a:.2f} * j3 + {b:.2f}\")\n",
    "# 根据拟合得到的直线方程计算预测的高一成绩值\n",
    "s1_pred = a * j3 + b\n",
    "# 设置matplotlib绘图时使用的字体为中文黑体，以便正确显示中文标题等\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']  \n",
    "# 解决matplotlib绘图时负号显示不正常的问题\n",
    "plt.rcParams['axes.unicode_minus'] = False \n",
    "# 绘制原始数据的散点图，颜色为蓝色，并添加标签\n",
    "plt.scatter(j3, s1, color='blue', label='原始数据')\n",
    "# 绘制拟合得到的回归线，颜色为红色，并添加标签，标签中包含回归直线方程\n",
    "plt.plot(j3, s1_pred, color='red', label=f'回归线: s1 = {a:.2f} * j3 + {b:.2f}')\n",
    "# 设置图表的标题\n",
    "plt.title('初三成绩 vs 高一成绩')\n",
    "# 设置x轴的标签\n",
    "plt.xlabel('初三成绩')\n",
    "# 设置y轴的标签\n",
    "plt.ylabel('高一成绩')\n",
    "# 显示图例，用于区分原始数据和拟合直线\n",
    "plt.legend()\n",
    "# 显示绘制好的图表\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "784f20d7-5cd9-43f5-9307-f1872f6ede72",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "信计221======================刘显婷==================================224180117\n",
      "特征矩阵数据类型： <class 'pandas.core.frame.DataFrame'>\n",
      "目标变量数据类型： <class 'pandas.core.series.Series'>\n",
      "回归方程系数： [-1.13055924e-01  3.01104641e-02  4.03807204e-02  2.78443820e+00\n",
      " -1.72026334e+01  4.43883520e+00 -6.29636221e-03 -1.44786537e+00\n",
      "  2.62429736e-01 -1.06467863e-02 -9.15456240e-01  1.23513347e-02\n",
      " -5.08571424e-01]\n",
      "回归方程截距： 30.24675099392412\n",
      "均方误差： 24.291119474973247\n",
      "判定系数R2： 0.6687594935356357\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('信计221======================刘显婷==================================224180117')\n",
    "# 导入所需的Python库\n",
    "import numpy as np  \n",
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt \n",
    "import seaborn as sns  \n",
    "# 用于增强绘图效果\n",
    "from sklearn.datasets import fetch_openml  \n",
    "# 用于加载数据集\n",
    "from sklearn.model_selection import train_test_split  \n",
    "# 用于数据集划分\n",
    "from sklearn.linear_model import LinearRegression  \n",
    "# 用于创建线性回归模型\n",
    "from sklearn.metrics import mean_squared_error, r2_score  \n",
    "# 用于评估模型性能\n",
    "import matplotlib  # 用于设置绘图参数\n",
    "# 设置中文字体，避免绘图时显示中文乱码\n",
    "matplotlib.rcParams['font.family'] = 'SimHei'\n",
    "# 加载波士顿房价数据集\n",
    "boston = fetch_openml(name='boston', version=1)\n",
    "# 获取特征矩阵和目标变量\n",
    "X = boston.data  # 特征矩阵，包含房屋的各种特征\n",
    "y = boston.target  # 目标变量，即房屋的中位数价格\n",
    "# 检查数据类型，确保是 numpy.ndarray\n",
    "print(\"特征矩阵数据类型：\", type(X))\n",
    "print(\"目标变量数据类型：\", type(y))\n",
    "# 如果数据类型不是 numpy.ndarray，将其转换为 numpy 数组\n",
    "X = np.array(X, dtype=np.float64)\n",
    "y = np.array(y, dtype=np.float64)\n",
    "# 将数据划分为训练集和测试集，测试集占20%\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "# 创建线性回归模型\n",
    "model = LinearRegression()\n",
    "# 训练模型\n",
    "model.fit(X_train, y_train)\n",
    "# 打印回归方程的系数和截距\n",
    "print(\"回归方程系数：\", model.coef_)\n",
    "print(\"回归方程截距：\", model.intercept_)\n",
    "# 进行预测\n",
    "y_pred = model.predict(X_test)\n",
    "# 打印均方误差和判定系数R2\n",
    "print(\"均方误差：\", mean_squared_error(y_test, y_pred))\n",
    "print(\"判定系数R2：\", r2_score(y_test, y_pred))\n",
    "# 绘制真实值与预测值对比图\n",
    "plt.plot(range(len(y_test)), y_test, label='真实值', color='blue')\n",
    "plt.plot(range(len(y_pred)), y_pred, label='预测值', color='red')\n",
    "plt.legend()\n",
    "plt.ylabel('房价（千美元）')\n",
    "plt.title('房价真实值与预测值对比')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a68a264e-b32b-48bf-879c-b573383b30e4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "信计221======================刘显婷==================================224180117\n",
      "回归系数: [ 0.          1.43902662 -0.00541601]\n",
      "截距: -1.103891491583198\n",
      "初三成绩为100的学生，预测的高一成绩为88.63871276551583\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('信计221======================刘显婷==================================224180117')\n",
    "# 导入所需的Python库\n",
    "import numpy as np  # 用于数学运算\n",
    "import matplotlib.pyplot as plt  # 用于绘图\n",
    "from sklearn.linear_model import LinearRegression  # 用于创建线性回归模型\n",
    "from sklearn.preprocessing import PolynomialFeatures  # 用于生成多项式特征\n",
    "# 读取数据文件路径\n",
    "file_path = r\"D:/Users/19202/Desktop/highschool.txt\"\n",
    "# 使用numpy的loadtxt函数读取数据，跳过第一行（通常是标题行）\n",
    "data = np.loadtxt(file_path, skiprows=1)\n",
    "# 提取特征（初三成绩）和目标变量（高一成绩）\n",
    "X = data[:, 0].reshape(-1, 1)  # 将一维数组转换为二维数组，以适应模型输入要求\n",
    "y = data[:, 1]  # 高一成绩\n",
    "# 创建一个多项式特征生成器，设置多项式的度数为2\n",
    "poly = PolynomialFeatures(degree=2) \n",
    "# 使用多项式特征生成器转换X，生成多项式特征\n",
    "X_poly = poly.fit_transform(X)\n",
    "# 创建线性回归模型\n",
    "model = LinearRegression()\n",
    "# 训练模型，使用转换后的多项式特征\n",
    "model.fit(X_poly, y)\n",
    "# 打印回归方程的系数和截距\n",
    "print(f\"回归系数: {model.coef_}\")\n",
    "print(f\"截距: {model.intercept_}\")\n",
    "# 假设有一个未知学生的初三成绩\n",
    "unknown_j3_score = 100 \n",
    "# 使用模型预测该学生的高一成绩\n",
    "predicted_s1_score = model.predict(poly.fit_transform([[unknown_j3_score]]))\n",
    "# 打印预测结果\n",
    "print(f\"初三成绩为{unknown_j3_score}的学生，预测的高一成绩为{predicted_s1_score[0]}\")\n",
    "# 绘制原始数据点\n",
    "plt.scatter(X, y, color='blue', label='原始数据')\n",
    "# 绘制多项式回归曲线\n",
    "plt.plot(X, model.predict(X_poly), color='red', label='多项式回归')\n",
    "# 绘制预测点\n",
    "plt.scatter([unknown_j3_score], [predicted_s1_score], color='green', label='预测点')\n",
    "# 设置x轴标签\n",
    "plt.xlabel('初三成绩')\n",
    "# 设置y轴标签\n",
    "plt.ylabel('高一成绩')\n",
    "# 设置图表标题\n",
    "plt.title('初三成绩与高一成绩的多项式回归')\n",
    "# 显示图例\n",
    "plt.legend()\n",
    "# 显示图表\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "66af5d5c-68a4-4725-b2cc-eb98b6540a4f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting pydotplus\n",
      "  Downloading pydotplus-2.0.2.tar.gz (278 kB)\n",
      "     ---------------------------------------- 0.0/278.7 kB ? eta -:--:--\n",
      "     ---------------------------------------- 0.0/278.7 kB ? eta -:--:--\n",
      "     ---------------------------------------- 0.0/278.7 kB ? eta -:--:--\n",
      "     ---------------------------------------- 0.0/278.7 kB ? eta -:--:--\n",
      "     ---------------------------------------- 0.0/278.7 kB ? eta -:--:--\n",
      "     - -------------------------------------- 10.2/278.7 kB ? eta -:--:--\n",
      "     -- ---------------------------------- 20.5/278.7 kB 162.5 kB/s eta 0:00:02\n",
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      "  Preparing metadata (setup.py): started\n",
      "  Preparing metadata (setup.py): finished with status 'done'\n",
      "Requirement already satisfied: pyparsing>=2.0.1 in c:\\anaconda\\lib\\site-packages (from pydotplus) (3.0.9)\n",
      "Building wheels for collected packages: pydotplus\n",
      "  Building wheel for pydotplus (setup.py): started\n",
      "  Building wheel for pydotplus (setup.py): finished with status 'done'\n",
      "  Created wheel for pydotplus: filename=pydotplus-2.0.2-py3-none-any.whl size=24575 sha256=5ae2ce8e46634c944b6bc2bba46ab926e7382a295b8adf88c07ff38edbd6e1fd\n",
      "  Stored in directory: c:\\users\\19202\\appdata\\local\\pip\\cache\\wheels\\77\\54\\7c\\c8077b6151c819495492300386cf9b151a954259d1a658c63b\n",
      "Successfully built pydotplus\n",
      "Installing collected packages: pydotplus\n",
      "Successfully installed pydotplus-2.0.2\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install pydotplus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b9a3d694-8f32-4d2c-b391-b657fc3c857f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'data': array([[5.1, 3.5, 1.4, 0.2],\n",
       "        [4.9, 3. , 1.4, 0.2],\n",
       "        [4.7, 3.2, 1.3, 0.2],\n",
       "        [4.6, 3.1, 1.5, 0.2],\n",
       "        [5. , 3.6, 1.4, 0.2],\n",
       "        [5.4, 3.9, 1.7, 0.4],\n",
       "        [4.6, 3.4, 1.4, 0.3],\n",
       "        [5. , 3.4, 1.5, 0.2],\n",
       "        [4.4, 2.9, 1.4, 0.2],\n",
       "        [4.9, 3.1, 1.5, 0.1],\n",
       "        [5.4, 3.7, 1.5, 0.2],\n",
       "        [4.8, 3.4, 1.6, 0.2],\n",
       "        [4.8, 3. , 1.4, 0.1],\n",
       "        [4.3, 3. , 1.1, 0.1],\n",
       "        [5.8, 4. , 1.2, 0.2],\n",
       "        [5.7, 4.4, 1.5, 0.4],\n",
       "        [5.4, 3.9, 1.3, 0.4],\n",
       "        [5.1, 3.5, 1.4, 0.3],\n",
       "        [5.7, 3.8, 1.7, 0.3],\n",
       "        [5.1, 3.8, 1.5, 0.3],\n",
       "        [5.4, 3.4, 1.7, 0.2],\n",
       "        [5.1, 3.7, 1.5, 0.4],\n",
       "        [4.6, 3.6, 1. , 0.2],\n",
       "        [5.1, 3.3, 1.7, 0.5],\n",
       "        [4.8, 3.4, 1.9, 0.2],\n",
       "        [5. , 3. , 1.6, 0.2],\n",
       "        [5. , 3.4, 1.6, 0.4],\n",
       "        [5.2, 3.5, 1.5, 0.2],\n",
       "        [5.2, 3.4, 1.4, 0.2],\n",
       "        [4.7, 3.2, 1.6, 0.2],\n",
       "        [4.8, 3.1, 1.6, 0.2],\n",
       "        [5.4, 3.4, 1.5, 0.4],\n",
       "        [5.2, 4.1, 1.5, 0.1],\n",
       "        [5.5, 4.2, 1.4, 0.2],\n",
       "        [4.9, 3.1, 1.5, 0.2],\n",
       "        [5. , 3.2, 1.2, 0.2],\n",
       "        [5.5, 3.5, 1.3, 0.2],\n",
       "        [4.9, 3.6, 1.4, 0.1],\n",
       "        [4.4, 3. , 1.3, 0.2],\n",
       "        [5.1, 3.4, 1.5, 0.2],\n",
       "        [5. , 3.5, 1.3, 0.3],\n",
       "        [4.5, 2.3, 1.3, 0.3],\n",
       "        [4.4, 3.2, 1.3, 0.2],\n",
       "        [5. , 3.5, 1.6, 0.6],\n",
       "        [5.1, 3.8, 1.9, 0.4],\n",
       "        [4.8, 3. , 1.4, 0.3],\n",
       "        [5.1, 3.8, 1.6, 0.2],\n",
       "        [4.6, 3.2, 1.4, 0.2],\n",
       "        [5.3, 3.7, 1.5, 0.2],\n",
       "        [5. , 3.3, 1.4, 0.2],\n",
       "        [7. , 3.2, 4.7, 1.4],\n",
       "        [6.4, 3.2, 4.5, 1.5],\n",
       "        [6.9, 3.1, 4.9, 1.5],\n",
       "        [5.5, 2.3, 4. , 1.3],\n",
       "        [6.5, 2.8, 4.6, 1.5],\n",
       "        [5.7, 2.8, 4.5, 1.3],\n",
       "        [6.3, 3.3, 4.7, 1.6],\n",
       "        [4.9, 2.4, 3.3, 1. ],\n",
       "        [6.6, 2.9, 4.6, 1.3],\n",
       "        [5.2, 2.7, 3.9, 1.4],\n",
       "        [5. , 2. , 3.5, 1. ],\n",
       "        [5.9, 3. , 4.2, 1.5],\n",
       "        [6. , 2.2, 4. , 1. ],\n",
       "        [6.1, 2.9, 4.7, 1.4],\n",
       "        [5.6, 2.9, 3.6, 1.3],\n",
       "        [6.7, 3.1, 4.4, 1.4],\n",
       "        [5.6, 3. , 4.5, 1.5],\n",
       "        [5.8, 2.7, 4.1, 1. ],\n",
       "        [6.2, 2.2, 4.5, 1.5],\n",
       "        [5.6, 2.5, 3.9, 1.1],\n",
       "        [5.9, 3.2, 4.8, 1.8],\n",
       "        [6.1, 2.8, 4. , 1.3],\n",
       "        [6.3, 2.5, 4.9, 1.5],\n",
       "        [6.1, 2.8, 4.7, 1.2],\n",
       "        [6.4, 2.9, 4.3, 1.3],\n",
       "        [6.6, 3. , 4.4, 1.4],\n",
       "        [6.8, 2.8, 4.8, 1.4],\n",
       "        [6.7, 3. , 5. , 1.7],\n",
       "        [6. , 2.9, 4.5, 1.5],\n",
       "        [5.7, 2.6, 3.5, 1. ],\n",
       "        [5.5, 2.4, 3.8, 1.1],\n",
       "        [5.5, 2.4, 3.7, 1. ],\n",
       "        [5.8, 2.7, 3.9, 1.2],\n",
       "        [6. , 2.7, 5.1, 1.6],\n",
       "        [5.4, 3. , 4.5, 1.5],\n",
       "        [6. , 3.4, 4.5, 1.6],\n",
       "        [6.7, 3.1, 4.7, 1.5],\n",
       "        [6.3, 2.3, 4.4, 1.3],\n",
       "        [5.6, 3. , 4.1, 1.3],\n",
       "        [5.5, 2.5, 4. , 1.3],\n",
       "        [5.5, 2.6, 4.4, 1.2],\n",
       "        [6.1, 3. , 4.6, 1.4],\n",
       "        [5.8, 2.6, 4. , 1.2],\n",
       "        [5. , 2.3, 3.3, 1. ],\n",
       "        [5.6, 2.7, 4.2, 1.3],\n",
       "        [5.7, 3. , 4.2, 1.2],\n",
       "        [5.7, 2.9, 4.2, 1.3],\n",
       "        [6.2, 2.9, 4.3, 1.3],\n",
       "        [5.1, 2.5, 3. , 1.1],\n",
       "        [5.7, 2.8, 4.1, 1.3],\n",
       "        [6.3, 3.3, 6. , 2.5],\n",
       "        [5.8, 2.7, 5.1, 1.9],\n",
       "        [7.1, 3. , 5.9, 2.1],\n",
       "        [6.3, 2.9, 5.6, 1.8],\n",
       "        [6.5, 3. , 5.8, 2.2],\n",
       "        [7.6, 3. , 6.6, 2.1],\n",
       "        [4.9, 2.5, 4.5, 1.7],\n",
       "        [7.3, 2.9, 6.3, 1.8],\n",
       "        [6.7, 2.5, 5.8, 1.8],\n",
       "        [7.2, 3.6, 6.1, 2.5],\n",
       "        [6.5, 3.2, 5.1, 2. ],\n",
       "        [6.4, 2.7, 5.3, 1.9],\n",
       "        [6.8, 3. , 5.5, 2.1],\n",
       "        [5.7, 2.5, 5. , 2. ],\n",
       "        [5.8, 2.8, 5.1, 2.4],\n",
       "        [6.4, 3.2, 5.3, 2.3],\n",
       "        [6.5, 3. , 5.5, 1.8],\n",
       "        [7.7, 3.8, 6.7, 2.2],\n",
       "        [7.7, 2.6, 6.9, 2.3],\n",
       "        [6. , 2.2, 5. , 1.5],\n",
       "        [6.9, 3.2, 5.7, 2.3],\n",
       "        [5.6, 2.8, 4.9, 2. ],\n",
       "        [7.7, 2.8, 6.7, 2. ],\n",
       "        [6.3, 2.7, 4.9, 1.8],\n",
       "        [6.7, 3.3, 5.7, 2.1],\n",
       "        [7.2, 3.2, 6. , 1.8],\n",
       "        [6.2, 2.8, 4.8, 1.8],\n",
       "        [6.1, 3. , 4.9, 1.8],\n",
       "        [6.4, 2.8, 5.6, 2.1],\n",
       "        [7.2, 3. , 5.8, 1.6],\n",
       "        [7.4, 2.8, 6.1, 1.9],\n",
       "        [7.9, 3.8, 6.4, 2. ],\n",
       "        [6.4, 2.8, 5.6, 2.2],\n",
       "        [6.3, 2.8, 5.1, 1.5],\n",
       "        [6.1, 2.6, 5.6, 1.4],\n",
       "        [7.7, 3. , 6.1, 2.3],\n",
       "        [6.3, 3.4, 5.6, 2.4],\n",
       "        [6.4, 3.1, 5.5, 1.8],\n",
       "        [6. , 3. , 4.8, 1.8],\n",
       "        [6.9, 3.1, 5.4, 2.1],\n",
       "        [6.7, 3.1, 5.6, 2.4],\n",
       "        [6.9, 3.1, 5.1, 2.3],\n",
       "        [5.8, 2.7, 5.1, 1.9],\n",
       "        [6.8, 3.2, 5.9, 2.3],\n",
       "        [6.7, 3.3, 5.7, 2.5],\n",
       "        [6.7, 3. , 5.2, 2.3],\n",
       "        [6.3, 2.5, 5. , 1.9],\n",
       "        [6.5, 3. , 5.2, 2. ],\n",
       "        [6.2, 3.4, 5.4, 2.3],\n",
       "        [5.9, 3. , 5.1, 1.8]]),\n",
       " 'target': array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "        0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "        2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "        2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]),\n",
       " 'frame': None,\n",
       " 'target_names': array(['setosa', 'versicolor', 'virginica'], dtype='<U10'),\n",
       " 'DESCR': '.. _iris_dataset:\\n\\nIris plants dataset\\n--------------------\\n\\n**Data Set Characteristics:**\\n\\n:Number of Instances: 150 (50 in each of three classes)\\n:Number of Attributes: 4 numeric, predictive attributes and the class\\n:Attribute Information:\\n    - sepal length in cm\\n    - sepal width in cm\\n    - petal length in cm\\n    - petal width in cm\\n    - class:\\n            - Iris-Setosa\\n            - Iris-Versicolour\\n            - Iris-Virginica\\n\\n:Summary Statistics:\\n\\n============== ==== ==== ======= ===== ====================\\n                Min  Max   Mean    SD   Class Correlation\\n============== ==== ==== ======= ===== ====================\\nsepal length:   4.3  7.9   5.84   0.83    0.7826\\nsepal width:    2.0  4.4   3.05   0.43   -0.4194\\npetal length:   1.0  6.9   3.76   1.76    0.9490  (high!)\\npetal width:    0.1  2.5   1.20   0.76    0.9565  (high!)\\n============== ==== ==== ======= ===== ====================\\n\\n:Missing Attribute Values: None\\n:Class Distribution: 33.3% for each of 3 classes.\\n:Creator: R.A. Fisher\\n:Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\\n:Date: July, 1988\\n\\nThe famous Iris database, first used by Sir R.A. Fisher. The dataset is taken\\nfrom Fisher\\'s paper. Note that it\\'s the same as in R, but not as in the UCI\\nMachine Learning Repository, which has two wrong data points.\\n\\nThis is perhaps the best known database to be found in the\\npattern recognition literature.  Fisher\\'s paper is a classic in the field and\\nis referenced frequently to this day.  (See Duda & Hart, for example.)  The\\ndata set contains 3 classes of 50 instances each, where each class refers to a\\ntype of iris plant.  One class is linearly separable from the other 2; the\\nlatter are NOT linearly separable from each other.\\n\\n|details-start|\\n**References**\\n|details-split|\\n\\n- Fisher, R.A. \"The use of multiple measurements in taxonomic problems\"\\n  Annual Eugenics, 7, Part II, 179-188 (1936); also in \"Contributions to\\n  Mathematical Statistics\" (John Wiley, NY, 1950).\\n- Duda, R.O., & Hart, P.E. (1973) Pattern Classification and Scene Analysis.\\n  (Q327.D83) John Wiley & Sons.  ISBN 0-471-22361-1.  See page 218.\\n- Dasarathy, B.V. (1980) \"Nosing Around the Neighborhood: A New System\\n  Structure and Classification Rule for Recognition in Partially Exposed\\n  Environments\".  IEEE Transactions on Pattern Analysis and Machine\\n  Intelligence, Vol. PAMI-2, No. 1, 67-71.\\n- Gates, G.W. (1972) \"The Reduced Nearest Neighbor Rule\".  IEEE Transactions\\n  on Information Theory, May 1972, 431-433.\\n- See also: 1988 MLC Proceedings, 54-64.  Cheeseman et al\"s AUTOCLASS II\\n  conceptual clustering system finds 3 classes in the data.\\n- Many, many more ...\\n\\n|details-end|\\n',\n",
       " 'feature_names': ['sepal length (cm)',\n",
       "  'sepal width (cm)',\n",
       "  'petal length (cm)',\n",
       "  'petal width (cm)'],\n",
       " 'filename': 'iris.csv',\n",
       " 'data_module': 'sklearn.datasets.data'}"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.datasets import load_iris\n",
    "\n",
    "data=load_iris()\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3ea48480-4324-46b3-9da5-1b9b28c84d4c",
   "metadata": {},
   "outputs": [],
   "source": [
    "x=data['data']\n",
    "y=data['target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5dbdd491-b68e-4882-8d54-9d62d50b8545",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.3,random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8da809f7-7ff3-4b51-b64f-df9eb90b4672",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(105, 4)\n",
      "(45, 4)\n",
      "(105,)\n",
      "(45,)\n"
     ]
    }
   ],
   "source": [
    "print(x_train.shape)\n",
    "print(x_test.shape)\n",
    "print(y_train.shape)\n",
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8c1265ab-ce5d-459e-ac2c-707c0114e9f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier(n_neighbors=3)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;KNeighborsClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.neighbors.KNeighborsClassifier.html\">?<span>Documentation for KNeighborsClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>KNeighborsClassifier(n_neighbors=3)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=3)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "model=KNeighborsClassifier(n_neighbors=3)\n",
    "model.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3dd3aa44-01a6-4642-ae5d-d04e8df9b7c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 1, 0, 2, 0, 2, 0, 1, 1, 1, 2, 1, 1, 1, 1, 0, 1, 1, 0, 0, 2, 1,\n",
       "       0, 0, 2, 0, 0, 1, 1, 0, 2, 1, 0, 2, 2, 1, 0, 2, 1, 1, 2, 0, 2, 0,\n",
       "       0])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred=model.predict(x_test)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c6684879-a5ad-4f18-9c30-d0afb9a5647b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 1, 0, 2, 0, 2, 0, 1, 1, 1, 2, 1, 1, 1, 1, 0, 1, 1, 0, 0, 2, 1,\n",
       "       0, 0, 2, 0, 0, 1, 1, 0, 2, 1, 0, 2, 2, 1, 0, 1, 1, 1, 2, 0, 2, 0,\n",
       "       0])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "225a7507-6fc4-4780-92a1-052648162fb5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict([[0.5,0.5,0.5,0.5]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c000190d-51b3-45a1-a84a-a13808a27b12",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9777777777777777"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(x_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "81c801ad-1102-4c23-9449-2b4f03f04298",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "信计221=========刘显婷===========224180117\n"
     ]
    }
   ],
   "source": [
    "print('信计221=========刘显婷===========224180117')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8499c2c6-5587-45c5-af00-85d6ba6ff160",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "信计221======================刘显婷==================================224180117\n",
      "特征矩阵数据类型： <class 'pandas.core.frame.DataFrame'>\n",
      "目标变量数据类型： <class 'pandas.core.series.Series'>\n",
      "回归方程系数： [-1.13055924e-01  3.01104641e-02  4.03807204e-02  2.78443820e+00\n",
      " -1.72026334e+01  4.43883520e+00 -6.29636221e-03 -1.44786537e+00\n",
      "  2.62429736e-01 -1.06467863e-02 -9.15456240e-01  1.23513347e-02\n",
      " -5.08571424e-01]\n",
      "回归方程截距： 30.24675099392412\n",
      "均方误差： 24.291119474973247\n",
      "判定系数R2： 0.6687594935356357\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('信计221======================刘显婷==================================224180117')\n",
    "# 导入所需的Python库\n",
    "import numpy as np  \n",
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt \n",
    "import seaborn as sns  \n",
    "# 用于增强绘图效果\n",
    "from sklearn.datasets import fetch_openml  \n",
    "# 用于加载数据集\n",
    "from sklearn.model_selection import train_test_split  \n",
    "# 用于数据集划分\n",
    "from sklearn.linear_model import LinearRegression  \n",
    "# 用于创建线性回归模型\n",
    "from sklearn.metrics import mean_squared_error, r2_score  \n",
    "# 用于评估模型性能\n",
    "import matplotlib  # 用于设置绘图参数\n",
    "# 设置中文字体，避免绘图时显示中文乱码\n",
    "matplotlib.rcParams['font.family'] = 'SimHei'\n",
    "# 加载波士顿房价数据集\n",
    "boston = fetch_openml(name='boston', version=1)\n",
    "# 获取特征矩阵和目标变量\n",
    "X = boston.data  # 特征矩阵，包含房屋的各种特征\n",
    "y = boston.target  # 目标变量，即房屋的中位数价格\n",
    "# 检查数据类型，确保是 numpy.ndarray\n",
    "print(\"特征矩阵数据类型：\", type(X))\n",
    "print(\"目标变量数据类型：\", type(y))\n",
    "# 如果数据类型不是 numpy.ndarray，将其转换为 numpy 数组\n",
    "X = np.array(X, dtype=np.float64)\n",
    "y = np.array(y, dtype=np.float64)\n",
    "# 将数据划分为训练集和测试集，测试集占20%\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "# 创建线性回归模型\n",
    "model = LinearRegression()\n",
    "# 训练模型\n",
    "model.fit(X_train, y_train)\n",
    "# 打印回归方程的系数和截距\n",
    "print(\"回归方程系数：\", model.coef_)\n",
    "print(\"回归方程截距：\", model.intercept_)\n",
    "# 进行预测\n",
    "y_pred = model.predict(X_test)\n",
    "# 打印均方误差和判定系数R2\n",
    "print(\"均方误差：\", mean_squared_error(y_test, y_pred))\n",
    "print(\"判定系数R2：\", r2_score(y_test, y_pred))\n",
    "# 绘制真实值与预测值对比图\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(y_test, label='真实值', color='blue', marker='o')\n",
    "plt.plot(y_pred, label='预测值', color='red', marker='x')\n",
    "plt.legend()\n",
    "plt.ylabel('房价（千美元）')\n",
    "plt.title('房价真实值与预测值对比')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0d3ffec8-f561-404f-9d6c-77fc2fd7ff99",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "信计221======================刘显婷==================================224180117\n",
      "回归系数: [ 0.          1.43902662 -0.00541601]\n",
      "截距: -1.103891491583198\n",
      "初三成绩为100的学生，预测的高一成绩为88.63871276551583\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('信计221======================刘显婷==================================224180117')\n",
    "# 导入所需的Python库\n",
    "import numpy as np  # 用于数学运算\n",
    "import matplotlib.pyplot as plt  # 用于绘图\n",
    "from sklearn.linear_model import LinearRegression  # 用于创建线性回归模型\n",
    "from sklearn.preprocessing import PolynomialFeatures  # 用于生成多项式特征\n",
    "# 读取数据文件路径\n",
    "file_path = r\"D:/Users/19202/Desktop/highschool.txt\"\n",
    "# 使用numpy的loadtxt函数读取数据，跳过第一行（通常是标题行）\n",
    "data = np.loadtxt(file_path, skiprows=1)\n",
    "# 提取特征（初三成绩）和目标变量（高一成绩）\n",
    "X = data[:, 0].reshape(-1, 1)  # 将一维数组转换为二维数组，以适应模型输入要求\n",
    "y = data[:, 1]  # 高一成绩\n",
    "# 创建一个多项式特征生成器，设置多项式的度数为2\n",
    "poly = PolynomialFeatures(degree=2) \n",
    "# 使用多项式特征生成器转换X，生成多项式特征\n",
    "X_poly = poly.fit_transform(X)\n",
    "# 创建线性回归模型\n",
    "model = LinearRegression()\n",
    "# 训练模型，使用转换后的多项式特征\n",
    "model.fit(X_poly, y)\n",
    "# 打印回归方程的系数和截距\n",
    "print(f\"回归系数: {model.coef_}\")\n",
    "print(f\"截距: {model.intercept_}\")\n",
    "# 假设有一个未知学生的初三成绩\n",
    "unknown_j3_score = 100 \n",
    "# 使用模型预测该学生的高一成绩\n",
    "predicted_s1_score = model.predict(poly.fit_transform([[unknown_j3_score]]))\n",
    "# 打印预测结果\n",
    "print(f\"初三成绩为{unknown_j3_score}的学生，预测的高一成绩为{predicted_s1_score[0]}\")\n",
    "# 绘制原始数据点\n",
    "plt.scatter(X, y, color='blue', label='原始数据')\n",
    "# 绘制多项式回归曲线\n",
    "X_line = np.linspace(X.min(), X.max(), 100).reshape(-1, 1)  # 创建一个连续的X值范围\n",
    "X_line_poly = poly.transform(X_line)  # 将连续的X值转换为多项式特征\n",
    "plt.plot(X_line, model.predict(X_line_poly), color='red', label='多项式回归')\n",
    "# 绘制预测点\n",
    "plt.scatter([unknown_j3_score], [predicted_s1_score[0]], color='green', label='预测点')\n",
    "# 设置x轴标签\n",
    "plt.xlabel('初三成绩')\n",
    "# 设置y轴标签\n",
    "plt.ylabel('高一成绩')\n",
    "# 设置图表标题\n",
    "plt.title('初三成绩与高一成绩的多项式回归')\n",
    "# 显示图例\n",
    "plt.legend()\n",
    "# 显示图表\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "9c0f1997-2827-486d-96b1-63653867b988",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "信计221 王志锋 224180125\n"
     ]
    }
   ],
   "source": [
    "print('信计221 王志锋 224180125')\n",
    "import numpy as np\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "# 定义特征集X和标签集y\n",
    "X = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]])\n",
    "y = np.array([1, 1, 1, 2, 2, 2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "eb28a246-6a83-4a5e-b561-28d369f5d92e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 创建高斯朴素贝叶斯模型\n",
    "clf = GaussianNB()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "ea2be566-f87a-427c-89d4-b018b4d3cfcc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-4 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-4 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-4 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-4 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-4 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-4 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-4 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-4 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-4 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-4 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GaussianNB()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;GaussianNB<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.naive_bayes.GaussianNB.html\">?<span>Documentation for GaussianNB</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>GaussianNB()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "GaussianNB()"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 使用特征集X和标签集y拟合模型\n",
    "clf.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "a4b72b80-1b66-49d8-82e2-7918c1e4d8ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 使用模型对新数据进行分类预测\n",
    "clf.predict([[-0.8, -1]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "a95433bf-8aba-4a80-a940-8acca577284f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[9.99999949e-01, 5.05653254e-08]])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 预测新数据属于每个类别的概率\n",
    "clf.predict_proba([[-0.8, -1]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "b65aee8e-9db5-4360-b7f1-d09c080ee88e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 评估模型对新数据的分类准确度\n",
    "clf.score([[-0.8, -1]], [1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "5e11b988-630a-43af-b59f-203d377895a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 评估模型对多个新数据的分类准确度\n",
    "clf.score([[-0.8, -1], [0, 0]], [1, 2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "bf253822-c5aa-4508-a8ee-9d1827b63e6d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "信计221======================刘显婷==================================224180117\n"
     ]
    }
   ],
   "source": [
    "print('信计221======================刘显婷==================================224180117')\n",
    "# 导入库\n",
    "import numpy as np\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "# 定义特征集X和标签集y\n",
    "X = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]])\n",
    "y = np.array([1, 1, 1, 2, 2, 2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "cd0f0c49-612f-4c3e-9b47-be6fd8a8e8d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 定义特征集X和标签集y\n",
    "X = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]])\n",
    "y = np.array([1, 1, 1, 2, 2, 2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "04081ddb-b97d-4750-aa56-8b01f88b2f33",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 创建高斯朴素贝叶斯模型\n",
    "clf = GaussianNB()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "d4dcdcd2-f539-497c-a40f-c69f876f64c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-5 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-5 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-5 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-5 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-5 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-5 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-5 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-5 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-5 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-5 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GaussianNB()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;GaussianNB<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.naive_bayes.GaussianNB.html\">?<span>Documentation for GaussianNB</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>GaussianNB()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "GaussianNB()"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 使用特征集X和标签集y对模型进行拟合\n",
    "clf.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "b4d6b2be-840a-4944-b0c5-119e53db57c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 使用拟合好的模型对新数据[[-0.8, -1]]进行分类预测\n",
    "clf.predict([[-0.8, -1]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "07ba53fb-ee76-4883-bfc7-a76ad3feb900",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[9.99999949e-01, 5.05653254e-08]])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 计算新数据[[-0.8, -1]]属于每个类别的概率\n",
    "clf.predict_proba([[-0.8, -1]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "30dfbd24-3475-438b-9d2f-b96d77e37e6e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 评估模型对新数据[[-0.8, -1]]的分类准确度，预期标签为1\n",
    "clf.score([[-0.8, -1]], [1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "ba0d137a-3fa8-4800-ac5a-90b53cf19dd7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 评估模型对多个新数据[[-0.8, -1], [0, 0]]的分类准确度，预期标签为[1, 2]\n",
    "clf.score([[-0.8, -1], [0, 0]], [1, 2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "52384e9b-f89b-46db-9e28-1d441fac62cc",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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